AI 中文总结
研究针对表面肌电图信号因特定主体神经肌肉特征难以泛化的问题，引入解缠模型分离特定任务和特定主体成分，提高手势识别和用户识别准确率，深入揭示生理机制，增强基于sEMG应用的鲁棒性。
AI 中文摘要
表面肌电图（sEMG）信号广泛用于人机接口中的手势识别和用户识别，但由于特定主体的神经肌肉特征，现有模型往往难以在个体间进行泛化。本研究引入一种解缠模型，从sEMG信号中分离出特定任务和特定主体成分，提高手势识别和用户识别系统的泛化能力与可解释性。实验结果表明，解缠后的成分显著提高了跨主体和跨天数的手势分类及用户识别准确率，优于传统方法。进一步分析显示，特定任务成分捕捉个体间相同手势的一致激活模式，特定主体成分反映独特神经肌肉特征用于用户识别，且特定主体成分跨天数相似度低于特定任务成分，导致用户识别准确率下降幅度大于手势识别。这些发现表明解缠方法不仅提高分类性能，还深入揭示sEMG信号生理机制，有望增强基于sEMG的应用在现实场景中的鲁棒性。
英文摘要
Surface electromyogram (sEMG) signals are widely used in human-machine interfaces for gesture recognition and user identification, but existing models often struggle to generalize across individuals due to subject-specific neuromuscular characteristics. This study introduces a disentanglement model that separates task-specific and subject-specific components from sEMG signals, thereby improving the generalization and interpretability of gesture recognition and user identification systems. Experimental results demonstrate that the disentangled components significantly improve the accuracy of both gesture classification and user identification across subjects and days, outperforming conventional methods under the same experimental conditions. Further analysis reveals that the task-specific components capture consistent activation patterns associated with the same gestures across individuals. In contrast, the subject-specific components reflect unique neuromuscular characteristics that can be used for user identification. Notably, the subject-specific components show lower similarity across days than the task-specific components, contributing to a greater decrease in user identification accuracy than in gesture recognition accuracy. These findings suggest that the disentanglement approach not only improves classification performance but also provides deeper insights into the physiological mechanisms underlying sEMG signals. The model's ability to isolate and interpret different neuromuscular components holds promise for enhancing the robustness of sEMG-based applications in real-world settings, including rehabilitation and user authentication. Our code is available at https://github.com/Open-EXG/HandDisentanglement.
